Project Overview

A global Broker-Dealer was adjudicated by regulators and suffered significant penalties for its ineffective transaction monitoring system and lack of risk-based suspicious activity monitoring.

• The organization had performed an upgrade of its transaction monitoring system which resulted in an unexpected heavy increase in their alert volume. This created a large backlog of alerts which the client was not prepared to handle.
• It was required to complete an end-to-end data validation testing and data quality assessments of its upstream, ETL and AML transaction monitoring environment.

Alius (A Matrix-IFS Company) was requested to perform analysis and provide possible recommendations for immediate steps that could be performed to mitigate risk in a manner consistent with regulatory requirements.

Alius SMEs’ worked with the Global Head of Risk and Compliance of a large multinational bank to complete a tactical review of its transaction monitoring system in order to develop risk-based suspicious activity monitoring, data validation testing, AML model testing, segmentation, and scenario tuning.

Project Solution 

• A multi-disciplined team was deployed in statistics, data science, and regulation to conduct focused data quality checks on 1.7 million records of transaction data in order to uncover and test a “golden set” of data to be used for further analysis.
• An exploratory data analytics was conducted with the use of data visualization tools and statistics to discover a hidden pattern in the data unknown to the client.
• Alius provided a menu of possible threshold changes, all fundamentally defensible and supported by statistical analysis.

Results

• Alius’ Data Visualization approach’ uncovered a hidden “network” of relationships among accounts, scenario thresholds, and alerts.
• The gains from the analysis were transitioned into ‘next steps’ to create an end-to-end risk-based transaction monitoring model.